AI cannot be perceived as RPA anymore — The rise of AI ??
Abhilash Shukla
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Artificial Intelligence was initially confused with RPA ( Robotic Process Automation ), which focused on automating repetitive tasks. RPA was essentially rule-based, handling processes like data entry, invoice processing, or customer service inquiries. It wasn’t “smart” in the way AI is today—it followed strict, predefined commands.
Back in 2000s, RPA was the closest thing to AI that many companies were implementing. It made work faster but didn’t involve any decision-making or learning. Think about early chatbots or even 微软 Clippy Assistant. These tools could execute commands but couldn't "learn" from their interactions.
Later, the focus moved toward training models with datasets. Around 2012, Deep Learning entered the scene, driven by access to more powerful computational resources and massive amounts of data. Companies like 谷歌 , Facebook , and IBM started leveraging deep learning for image recognition, language processing, and more. This period marked a shift from task automation to AI that could “learn” from patterns.
One of the early breakthrough examples was IBM Watson, which made headlines when it won Jeopardy! in 2011. Watson was a leap forward because it wasn’t just following rules, it was trained on vast amounts of data and could understand and answer complex questions by analyzing patterns. But this was still just the beginning. While Watson was showing what training could achieve, AI still needed massive datasets and human oversight.
Then came the neural networks revolution. Companies like Google DeepMind pushed the boundaries of what AI could do by creating models that didn’t just learn, they optimized themselves. DeepMind’s AlphaGo made history in 2016 by defeating the world champion in the game of Go, a feat many believed was impossible for AI due to the game’s complexity. This was a clear sign that AI was no longer just following rules or reacting, it was learning, adapting, and predicting.
Today, we’re in the era of Generative AI . GenAI doesn't just learn from data, it creates. Take OpenAI GPT models or DALL·E as examples, GPT-4 can generate human-like text, based on its training, creating everything from essays to coding solutions. It’s a fundamental shift from task automation to creative generation. DALL·E goes a step further by generating images from text prompts, showcasing how AI can now create entirely new forms of content.
These advancements are the result of years of perseverance, incremental improvements, and overcoming major challenges. One significant hurdle was the availability of massive datasets and the computational power required to process them. Without the explosion of Big Data and advancements in Cloud Computing , today’s AI models would have been still a theory.
In terms of real-world impact, AI is no longer a futuristic dream, it’s a daily reality. Netflix uses GenerativeAI to predict what shows you might like. Spotify curates playlists based on your listening history, using Machine Learning Algorithms to generate recommendations. Tesla autonomous driving relies on AI trained from millions of miles of driving data.
Although with all of these great stories, the flip side we have seen with the bias with the datasets. If you remember in 2018, 亚马逊 had to scrap an AI recruitment tool because it favored male candidates over female ones—highlighting the risks of using biased datasets for training AI.
Nevertheless, what started as rule-based automation has evolved into something much more complex and powerful. AI generates original content, makes real-time decisions, and adapts based on the data it is fed — this is happening. This transformation from RPA to Generative AI is a proof of the hardwork of researchers, engineers, and data scientists, pushing the limits of what machines can achieve.
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Disclaimer: My views and interpretations are my own. They are based on past events and news but are only my personal viewpoints. These should be seen as perspectives, not endorsements. No company is involved, and these views are not related to my employers.
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